Since 2023, Moderna and Korea University have been developing a new mRNA vaccine for hantavirus. The work has been promising so far, but a finished product isn't likely coming any time soon. US-based pharmaceutical company Moderna confirmed that it has been working on the development of hantavirus vaccines in collaboration with the Vaccine Innovation Center of Korea University College of Medicine (VIC-K). This comes after an outbreak of hantavirus occurred on a Dutch cruise ship that sailed from Argentina and disembarked its passengers and crew in the Canary Islands on May 10. At least three people aboard the MV died, and several cases were reported as serious.

When you purchase through links in our articles, we may earn a small commission. Who actually manufactures AmazonBasics batteries? AmazonBasics batteries are so cheap, you might be skeptical of their quality and performance. When AmazonBasics launched back in 2009, batteries were among the initial line-up of products--and they're still one of the best, most classic impulse buys of this white-label brand. Mainly sold in packs ranging from 8 to 300 batteries at extremely affordable prices, they've become the go-to value battery brand for day-to-day needs.

PCWorld warns that high DDR5 RAM demand is driving counterfeiters to sell fake modules with plastic chips glued onto circuit boards. These fraudulent listings often appear as'junk' or'untested' items to prevent returns, with buyers discovering the scam only after installation causes system failures. This mirrors previous scams involving fake GPUs and re-lidded CPUs, highlighting the need for extreme caution when purchasing PC components from secondary markets.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Who is Gerhard Schroeder, Putin's pick for Ukraine peace talks mediation? Russian President Vladimir Putin has suggested that former German Chancellor Gerhard Schroeder could coordinate talks with the European Union to secure a peace deal in Ukraine - a proposal met with scepticism by EU officials. European Council President Antonio Costa said recently he believed there was "potential" for the EU to negotiate with Russia and to discuss the future of Europe's security architecture. A day later, the Russian leader said the four-year-old war may be "coming to an end", adding that he was ready to hold direct talks with his Ukrainian counterpart, Volodymyr Zelenskyy, in Moscow or a neutral country. Speaking after Saturday's celebrations for Victory Day, which marks Russia's victory over Nazi Germany in 1945 at the end of World War II, Putin added he would be willing to meet Zelenskyy only once the terms of a peace agreement had already been settled.

Things to Do in L.A. Fears of an AI breakthrough force the U.S. and China to talk Quiet discussions have taken place ahead of President Trump's state visit to China this week to explore reviving talks on an emergency channel, officials told The Times. This is read by an automated voice. Please report any issues or inconsistencies here . Discussions have taken place ahead of President Trump's state visit to China to explore reviving talks on an emergency channel for AI matters between Washington and Beijing, officials say. Any talks between the United States and China over AI regulations will be fraught with suspicion and risk.

SoftBank will partner with South Korea's Cosmos Lab and DeltaX to enable mass production of large-scale battery cells from the fiscal year starting next April. SoftBank Group's mobile unit said it plans to begin large-scale battery cell manufacturing at its plant in Sakai, Osaka Prefecture, to address growing power demand for AI services. SoftBank Corp. will partner with South Korea's Cosmos Lab and DeltaX to enable mass production from the fiscal year starting next April, the company said in a statement Monday. The aim is to output energy storage systems at a scale of one gigawatt-hour per year, SoftBank said, which would make it one of the largest facilities in Japan, according to data from BloombergNEF. SoftBank could scale up to a capacity of several GWh, Bloomberg reported last month.

We study the spectral properties of sample covariance matrices constructed from pooled sequence representations, where token embeddings are drawn from a fixed two-class Gaussian mixture table and pooled via (fixed) attention weights. Working in the high-dimensional regime $d,V,N\to\infty$ with $d/V\toδ$ and $d/N\toγ$, we derive exact characterizations of the limiting eigenvalue distribution, outlier eigenvalues, and eigenvector alignment with the hidden signal. The bulk spectrum follows a non-Marchenko--Pastur law given by the free multiplicative convolution $κ(MP_δ\boxtimes MP_γ)$, reflecting the finite vocabulary structure. Signal recovery undergoes two successive BBP-type phase transitions characterized by the scalars: $δ,γ,α=w^{\top} R w$ and $κ=\|w\|^2$, where $w$ denotes the attention pooling weights and $R$ the positional correlation matrix. An aftermath of our analysis demonstrates that the optimal attention weights maximizing the signal-to-noise ratio $α/κ$ are given by the (normalized) top eigenvector of $R$, and we show (as a particular case of our analysis) that parameter-free causal self-attention with $τ/d$ score scaling yields deterministic harmonic weights that improve signal recovery over mean pooling whenever early tokens carry more signal. Extensive simulations confirm sharp agreement between theory and finite-dimensional experiments.

LLM-as-a-Judge evaluation has become a standard tool for assessing base model performance. However, characterizing performance via the naive estimator, i.e., raw judge outputs, is systematically biased. Recent work has proposed estimators to correct this bias, but their reliability depends critically on judge quality and, for model comparisons, on calibration stability. Sharing calibration across compared models is practically attractive but can introduce severe bias, including cases where the comparison estimate points in the wrong direction with high apparent confidence. We study these failure modes through analytical results, simulations over judge quality ($J$) and cross-model calibration instability ($ΔJ$), and a real-data MMLU-Pro case study with sign reversal. We propose $J$ and $ΔJ$ as diagnostics for when corrected estimates, especially shared-calibration comparisons, are likely unreliable, and provide reporting guidance for LaaJ evaluation.

We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture whose layers are definable in an o-minimal structure has finite sample complexity in the agnostic PAC setting, even with unbounded parameters. This covers standard fixed-size MLPs, CNNs, GNNs, and transformers with fixed sequence length, together with the operations and layers typically used in such architectures, including linear projections, residual connections, attention mechanisms, pooling layers, normalization layers, and admissible positional encodings. Hence, distribution-free learnability for modern non-recurrent architectures is not an exceptional property of particular activations or architecture-specific VC arguments, but a consequence of tame feedforward computation. Our results reposition finite-sample PAC learnability as a baseline rather than a differentiator: they shift the focus of architectural comparison toward inductive biases, symmetries and geometric priors, scalability, and optimization behaviour.

Constructing valid and informative conformal prediction regions for multi-dimensional outputs remains a fundamental challenge. While conformal prediction provides finite-sample, distribution-free coverage guarantees, its practical performance critically depends on the choice of nonconformity score. Existing approaches often rely on restrictive geometric assumptions or require explicit likelihood evaluation and invertible transformations, limiting their applicability in complex generative settings. In this work, we introduce TRACE (TRansport Alignment Conformal Estimation), a conformal prediction framework that defines nonconformity through transport alignment in diffusion and flow matching models. Rather than evaluating likelihoods, we measure how well a candidate output aligns with the learned generative dynamics by averaging denoising or velocity-matching errors along stochastic transport trajectories. The resulting transport-based scores are scalar-valued and can be calibrated using split conformal prediction, yielding valid marginal coverage under exchangeability. We further analyze the statistical properties of the proposed scores and their sensitivity to computational budget. Experiments on synthetic and real datasets demonstrate valid coverage and show that the resulting regions adapt naturally to multimodal and non-convex conditional distributions.